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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,338 Documents
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Solar photovoltaic power system for Bungin Island Novi Azman; Rudi Naufal Fadhilah; Muhammad Ismail
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp7-17

Abstract

Bungin Island currently relies on diesel -based electricity power generation, resulting in limited supply reliability. This study evaluates the technical feasibility of an off-grid solar photovoltaic (PV) system integrated with lithium-ion battery energy storage (BESS) using photovoltaic system (PVsyst) simulation. Based on measured load demand of 2,830 kWh/day and NASA solar resource data, the optimized configuration consist of a 0.713 MWp PV array and an 11.54 MWh battery system. Simulation result indicates an annual PV production (E_Array) of 1,258.3 MWh/year. Of this, 1,032.9 MWh/year is delivered to the load, with excess energy of 120.84 MWh/year and a limited unmet load of 13.7 MWh/year (1.33%). The system achieves a solar fraction 98.67%, a performance ratio (PR) of 77.6% and a capacity factor of 17.34%. These results demonstrate that a properly sized PV-battery configuration can reliably replace diesel generation, providing a robust framework for high-renewable electrification in densely populated small islands.
Pulmonary nodule in CT image quantification by using pulmonary nodules magnitude ratio Asharani Ramadas; Chidananda Murthy Melekote Vinayakamurth
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp103-113

Abstract

The growth rate of the Pulmonary nodule increases the doubling time of the Pulmonary nodule, which is a significant indicator of malignancy. The mean diameter measurement of the Pulmonary nodule contributes to the assessment of lung cancer based on the doubling time. Small nodule size, partial volume effect, irregular shape, juxtapleural, and juxtavascular nodules are difficult to measure. This work addresses these measurement challenges using an image processing pipeline consisting of image segmentation and quantification. The computed tomography (CT) image preprocessing, segmentation, edge detection, and nodule measurement framework is proposed to extract the nodules from the CT image and then quantify them by measuring their mean diameter. A novel pulmonary nodules magnitude ratio (PNMR) is proposed to establish the sattastical relationship between the nodule and corresponding parenchyma size. The LNMR is evaluated against the synthetic nodules that express the nodule growth rate. This work contributes automatic nodule detection, semiautomatic nodule measurement, and PNMR evaluation for more reliable detection and quantification of Pulmonary nodules.
Adaptive vector control of PV-fed induction motor using boost split-source inverter without MPPT Romaissa Hamdi; Yassine Beddiaf; Djamel Sakri; Daoud Rezzak; Hassina Slimani
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp39-62

Abstract

This paper proposes an adaptive vector control strategy for a photovoltaic (PV)-fed induction motor using a boost split-source inverter (BSSI). Unlike conventional PV conversion systems, the proposed topology combines voltage boosting and DC–AC conversion in a single stage, reducing component count, switching losses, and overall system complexity. In addition, the system operates without a maximum power point tracking (MPPT) algorithm, simplifying the control structure while maintaining stable operation under varying environmental conditions. The proposed control approach integrates sliding mode control (SMC) for robust DC-bus voltage regulation and an adaptive proportional–integral (API) speed controller based on Lie derivative theory for online tuning of controller gains according to the speed tracking error. An active DC-link protection mechanism is also introduced to prevent overvoltage during transient conditions. The main contribution of this paper lies in the combination of the BSSI topology with a hybrid adaptive control framework to improve robustness, dynamic performance, and system reliability under irradiance, temperature, and load variations. Simulation and experimental results demonstrate fast dynamic response, reduced speed and voltage oscillations, accurate speed tracking, and superior performance compared with conventional vector control methods.
A mathematical model for IoT malware propagation with adaptive patching strategy based on R₀: a simple optimal control approach Dwi Ely Kurniawan; Sarifuddin Madenda; Eri Prasetyo Wibowo
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp219-232

Abstract

This paper presents a mathematical framework for modeling and controlling internet of things (IoT) malware propagation via an adaptive patching strategy governed by the real-time basic reproduction number R₀. We introduce the SEIR-P (susceptible–exposed–infected–recovered–patched) model, where the patching control rate u(t) is a sigmoid feedback function of R₀(t). All epidemiological parameters are calibrated from three empirical malware captures of the IoT-23 dataset (Stratosphere laboratory, Czech Technical University) a publicly available labeled collection of real IoT network traffic comprising 23 captures from infected and benign devices: CTU-IoT-1 (Mirai), CTU-IoT-9 (Torii), and CTU-IoT-17 (IRCBot) yielding the first empirically grounded parameter set for SEIR-type IoT epidemic models with confidence intervals. The optimal control problem is formulated via pontryagin’s maximum principle (PMP), and a closed-form R₀(u) expression is derived via the next-generation matrix (NGM), yielding the critical threshold u_crit = 0.142 day⁻¹. Five comparative simulation scenarios over a 365-day horizon show that the proposed R₀-adaptive strategy achieves a 91.3% reduction in peak infection (360 vs. 4,142 devices), eradicates malware by day 179, and attains the highest cost-effectiveness index (CEI = 1.142). Global asymptotic stability of the disease-free equilibrium under u*(t) is proven via Lyapunov’s method and LaSalle’s Invariance Principle. PRCC sensitivity analysis identifies u_max and β as dominant parameters. This closed-loop framework bridges the gap between abstract epidemic theory and deployable IoT security management.
An intelligent speed controller for indirect vector-controlled induction motor with high efficiency taking core loss into account Yassina Mederharhet; Leila Boukarana
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp28-38

Abstract

To address high-performance drive operations, this work details a genetic algorithm (GA)-tuned proportional integral (PI) control strategy applied to sensorless indirect vector-controlled induction motors (IM), explicitly embedding core loss dynamics within the loop. Although GA-based PI tuning methods have been extensively studied, most existing approaches neglect iron loss dynamics, leading to reduced modeling accuracy and suboptimal energy efficiency. The proposed method simultaneously optimizes PI speed controller gains using GA while integrating core loss resistance into the motor model. This combined optimization enhances both dynamic performance and energy efficiency under varying load and speed conditions. Simulation results demonstrate that the proposed PI-GA controller reduces settling time by 62% compared to a classical PI controller, while overshoot decreases from 18% to 5%. Total harmonic distortion (THD) is limited to 3.4%, and iron losses are reduced by approximately 15%, resulting in an overall efficiency improvement up to 95.2%. Comparative analysis confirms the robustness and superiority of the proposed strategy, highlighting its suitability for high-performance and energy-efficient IM drive applications.
Layer-wise adaptive structured pruning via genetic algorithms with taylor-based proxy fitness Anh-Truong Vo; Hoang-Loc Tran; Dinh-Duy Phan; Duc-Lung Vu
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp314-324

Abstract

Deploying deep convolutional neural networks (CNNs) on edge devices requires balancing model accuracy and computational efficiency. While structured pruning limits inference costs by removing redundant filters, most methods apply a rigid, global criterion, ignoring the distinct representational roles of individual layers. This yields suboptimal results, especially under aggressive compression where over-pruning degrades performance. To address this limitation, we propose an adaptive structured pruning framework based on genetic algorithms (GAs) that jointly optimizes layer-wise pruning ratios and strategies. Each layer independently selects between min-importance and median-rank pruning, enabling the exploration of tailored strategy combinations. A training-free taylor based proxy fitness function ensures efficient candidate evaluation without re peated fine-tuning. After fine-tuning the selected architecture, experiments on VGG16 demonstrate that our method achieves 92.78 ± 0.28% accuracy (over 50 independent runs) with a 70.0 ± 3.2% MACsreductiononCIFAR-10, andmaintains 71.82% accuracy on CIFAR-100. These results demonstrate competitive performance compared to existing pruning methods while achieving substantial computational cost reduction.
Evaluating oversampling methods for imbalanced Arabic dialect identification Maulana Ihsan Ahmad; Aina Musdholifah; Arif Nurwidyantoro
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp259-270

Abstract

This study investigates whether oversampling is a reliable solution for severe class imbalance in Arabic dialect identification. Using the Shami Corpus as a controlled testbed, we demonstrate that conventional oversampling often fails in high-dimensional sparse text spaces, but density based cluster filtering can effectively resolve this. We conduct a comparative evaluation of SMOTE, clustering-guided variants (ASTRA-SMOTE and SMOTE-RADIANT), and a cost-sensitive ClassWeight approach under an identical 5,644-dimensional feature-engineering pipeline using LightGBM and XGBoost. On the held-out test set, standard SMOTE and class weighting frequently distorted decision boundaries, yielding inconsistent gains across models. In contrast, SMOTE-RADIANT yields a statistically significant macro-F1 improvement for LightGBM (0.8539 vs. 0.8526 on the original data) with a large effect size (r = 0.511), successfully rescuing minority dialects without degrading the majority class. These findings suggest that while oversampling is not universally reliable in sparse text spaces, coupling it with density-based noise neutralization (RADIANT) provides a robust and interpretable alternative to deep learning models. This study provides methodological clarity and reproducible guidance for fair and inclusive Arabic NLP systems.
A hybrid approach for multi-view MRI Alzehimer’s detection using convolutional neural networks and bio-inspired algorithms Iheb Chemss El Dine Hagani; Nacéra Benamrane; Lakhdar Sais
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp192-206

Abstract

Alzheimer’s disease (AD) is a neurodegenerative disorder that remains incurable to date. Therefore, the most important step in treatment remains the early detection of the signs indicating its presence. The sooner these signs are discovered, the sooner preventative care can be administered. Convolutional neural networks (CNNs) have demonstrated impressive performance in medical image analysis; however, they often suffer from suboptimal manual tuning of their hyperparameters. Therefore, we opted for a hybrid method combining them with genetic algorithms (GA) and particle swarm optimization (PSO) to automatically optimize architectures and fusion weights for improved AD detection. Using data obtained from ADNI and Kaggle, our approach achieved 87.4% accuracy, surpassing classical CNNs of the same size and depth. These results highlight the potential of evolutionary optimization for developing reliable diagnostic tools.
Hybrid machine learning framework for anomaly detection in industrial IoT environments I Dewa Made Widia; Toni Anwar
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp345-354

Abstract

The industrial internet of things (IIoT) has become a core component of Industry 4.0, enabling highly connected and data-driven industrial systems while simultaneously increasing exposure to cyber threats. Conventional intrusion detection systems (IDS), especially rule-based and signature-driven approaches, often struggle to cope with the dynamic, high-dimensional, and heterogeneous nature of IIoT traffic. This study proposes a hybrid anomaly detection framework that integrates autoencoder, isolation forest, and long short-term memory (LSTM) models using a weighted decision fusion strategy. Each component contributes complementary capabilities, including nonlinear feature learning, efficient outlier detection, and temporal pattern modeling. The framework is evaluated on the botnet of things (BoT-IoT) dataset and further validated using IoT-23. Experimental results show that the proposed hybrid approach achieves a precision of 0.999, recall of 0.970, and an F1-score of 0.985, while maintaining a false-negative rate below 0.001%. Although its area under the curve (AUC) is slightly lower than that of a standalone light gradient boosting machine (LightGBM) baseline, the hybrid framework consistently reduces missed detections, making it well suited for reliable real-time IIoT security monitoring.
Auto-generated unit testing using PCA-aDynaMOSA Made Raja Adi Surya Saputra; Maria Seraphina Astriani
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 1: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i1.pp233-249

Abstract

Automated test case generation is essential for improving software quality; however, many-objective search-based testing approaches often experience scalability issues when the number of test objectives increases. This condition leads to slower convergence, higher computational effort, and reduced ability to cover complex program structures. To address this gap, this study proposes an enhanced version of the aDynaMOSA algorithm by incorporating principal component analysis (PCA) to reduce redundant objectives during the search process. The proposed method preserves essential objective information while eliminating dependency noise that typically slows the evolutionary search. Experiments were conducted using the SF110 benchmark dataset through EvoSuite, and the approach was compared with standard many-objective search strategies. The findings demonstrate that PCA-based objective reduction can improve performance, achieving up to 3.87%, 5.50%, and 3.75% for coverage of line, branch, and mutation respectively. These results indicate that dimensionality reduction can significantly enhance scalability and efficiency in automated evolutionary test generation, providing a foundation for future adaptive and hybrid optimization strategies.

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